About the Role

The Manager, Business Performance & Analytics is responsible for enabling the Company’s analytics, business intelligence, and credit risk functions through the design, development, and operationalization of enterprise-grade data products, analytics platforms, and data infrastructure.The role bridges data engineering, analytics enablement, and software engineering — ensuring that credit scoring systems, performance dashboards, and analytical platforms are delivered through secure, scalable, and well-governed data solutions.

This role is therefore critical in ensuring the bank maintains a robust data backbone, fosters cross-functional collaboration, and continuously evolves its analytics capabilities to meet regulatory, commercial, and customer demands.

Key Responsibilities

Data Products & Back-End Development

• Lead the design and development of data services that power analytics platforms, credit scoring engines, and business decision systems.
• Build and maintain RESTful APIs to serve curated data, analytical outputs, and scoring results to internal and external consumers.
• Oversee integration of analytics back-end services with data warehouses, data lakes, and third-party systems.
• Enforce high standards of code quality, testing, documentation, and deployment automation across the team.
• Ensure best practices in application security, authentication, authorisation, logging, and monitoring are consistently applied.

Data & Analytics Enablement

• Partner with data engineers and analysts to productionise data pipelines, feature stores, and analytics workloads.
• Provide reliable back-end infrastructure to enable analytics and credit risk teams across key functions:
  • Credit scoring and limit management
  • Portfolio analytics and performance reporting
  • Regulatory and compliance reporting
• Ensure all data exposed through APIs and platforms aligns with agreed data definitions, governance standards, and quality controls.

Team Leadership & Capability Development

• Lead, mentor, and manage back-end developers, data engineers, and analytics engineers, setting clear objectives and supporting career growth.
• Foster a culture of ownership, engineering excellence, continuous improvement, and collaborative delivery.
• Conduct regular performance reviews and support skill progression plans across the team.
• Allocate resources across projects to ensure optimal workload balance and timely, high-quality delivery.

KPI Tracking & Performance Management

• Define, monitor, and report on team and platform KPIs, including:
  • System availability and response times
  • Data pipeline reliability and data quality metrics
  • Delivery timelines and backlog health
  • Adoption and usage of analytics services
• Establish dashboards and regular performance reviews to drive transparency, accountability, and continuous improvement.
• Use KPI insights to inform process improvements, resource prioritisation, and investment decisions.

Stakeholder & Cross-Functional Collaboration

• Work closely with Risk, Credit, Finance, Retail, Commercial, Technology, and Operations teams to translate business needs into technical solutions.
• Communicate complex technical concepts clearly and concisely to non-technical stakeholders and senior leadership.
• Support vendor engagement and ensure external solutions align with internal architecture and governance frameworks.

Governance, Risk & Compliance

• Ensure all back-end and data solutions comply with data privacy, security, and applicable regulatory requirements.
• Maintain auditability and traceability for analytics outputs, with particular rigour for credit scoring and decisioning systems.
• Contribute to enterprise data governance, architecture standards, and technology best practices.

Principal outputs for this role

• Productionised data products and feature stores that enable reliable, real-time analytics workloads.
• Performance dashboards and KPI frameworks tracking system health, pipeline reliability, and team delivery.
• Well-documented, tested, and deployment-ready code meeting engineering excellence standards.
• Audit-ready analytics outputs with full traceability for credit scoring and regulatory reporting.
• A high-performing, well-developed analytics engineering team with clear objectives and growth pathways.

Qualifications

• Bachelor’s degree in Data Science, Actuarial Science, Statistics, Mathematics, Computer Science, Business Analytics, or a related field (required).
• Master’s degree or postgraduate qualification in a relevant discipline is an added advantage.
• 5–8 years of progressive experience in data analytics, data engineering, or back-end software engineering.
• Experience in overseeing cross-functional teams.
• Proven track record of building and operationalising data systems and back-end platforms in enterprise or regulated environments.
• Demonstrated experience in delivering dashboards, automated pipelines, and predictive or scoring models in a commercial setting.
• Experience supporting analytics platforms, credit scoring, or decisioning systems within financial services is a strong advantage.
• Solid understanding of data governance, data warehousing, and regulatory compliance in the banking or financial sector.

Competencies

Technical Competencies

• Knowledge of KPI tracking methodologies, reporting automation, and visualisation best practices.
• Data Platforms: Strong command of SQL; experience with data warehouses, data lakes, and analytics data modelling.
• BI & Visualisation: Proficiency in Power BI, Tableau, or equivalent tools for executive-facing dashboards and self-service analytics.
• Cloud Platforms: Hands-on experience with cloud-based environments (AWS, Azure, or Google Cloud).
• Big Data & Pipelines: Familiarity with Apache Spark, Kafka, or Hadoop for large-scale data processing.
• DevOps & Quality: Experience with CI/CD pipelines, version control (Git), testing frameworks, and monitoring tools.
• Machine Learning: Working knowledge of ML model deployment, feature engineering, and model monitoring in production.
• Back-End Development: Proficiency in Python (Django/FastAPI), RESTful API design, authentication and authorisation patterns.

General Competencies

• Analytical Thinking: Ability to break down complex datasets and derive clear, actionable conclusions.
• Attention to Detail: High standards for data accuracy, consistency, and reporting integrity.
• Communication & Influence: Skilled at presenting complex findings to non-technical audiences in plain, persuasive language.
• Collaboration: Comfortable engaging across teams and business units to gather requirements and deliver solutions.
• Commercial Awareness: Strong understanding of banking products, customer behaviour, and business performance drivers.
• Adaptability: Able to manage multiple priorities and respond effectively to shifting business needs and technologies.
• Integrity & Compliance: Committed to data ethics, customer privacy, and adherence to regulatory standards.

• Lead the design and development of data services that power analytics platforms, credit scoring engines, and business decision systems.
• Build and maintain RESTful APIs to serve curated data, analytical outputs, and scoring results to internal and external consumers.
• Oversee integration of analytics back-end services with data warehouses, data lakes, and third-party systems.
• Enforce high standards of code quality, testing, documentation, and deployment automation across the team.
• Ensure best practices in application security, authentication, authorisation, logging, and monitoring are consistently applied.
• Partner with data engineers and analysts to productionise data pipelines, feature stores, and analytics workloads.
• Provide reliable back-end infrastructure to enable analytics and credit risk teams across key functions: Credit scoring and limit management, Portfolio analytics and performance reporting, Regulatory and compliance reporting.
• Ensure all data exposed through APIs and platforms aligns with agreed data definitions, governance standards, and quality controls.
• Lead, mentor, and manage back-end developers, data engineers, and analytics engineers, setting clear objectives and supporting career growth.
• Foster a culture of ownership, engineering excellence, continuous improvement, and collaborative delivery.
• Conduct regular performance reviews and support skill progression plans across the team.
• Allocate resources across projects to ensure optimal workload balance and timely, high-quality delivery.
• Define, monitor, and report on team and platform KPIs, including: System availability and response times, Data pipeline reliability and data quality metrics, Delivery timelines and backlog health, Adoption and usage of analytics services.
• Establish dashboards and regular performance reviews to drive transparency, accountability, and continuous improvement.
• Use KPI insights to inform process improvements, resource prioritisation, and investment decisions.
• Work closely with Risk, Credit, Finance, Retail, Commercial, Technology, and Operations teams to translate business needs into technical solutions.
• Communicate complex technical concepts clearly and concisely to non-technical stakeholders and senior leadership.
• Support vendor engagement and ensure external solutions align with internal architecture and governance frameworks.
• Ensure all back-end and data solutions comply with data privacy, security, and applicable regulatory requirements.
• Maintain auditability and traceability for analytics outputs, with particular rigour for credit scoring and decisioning systems.
• Contribute to enterprise data governance, architecture standards, and technology best practices.

• Strong command of SQL
• Experience with data warehouses, data lakes, and analytics data modelling
• Proficiency in Power BI, Tableau, or equivalent tools
• Hands-on experience with cloud-based environments (AWS, Azure, or Google Cloud)
• Familiarity with Apache Spark, Kafka, or Hadoop
• Experience with CI/CD pipelines, version control (Git), testing frameworks, and monitoring tools
• Working knowledge of ML model deployment, feature engineering, and model monitoring in production
• Proficiency in Python (Django/FastAPI)
• RESTful API design
• Authentication and authorisation patterns
• Analytical Thinking
• Attention to Detail
• Communication & Influence
• Collaboration
• Commercial Awareness
• Adaptability
• Integrity & Compliance

• Bachelor’s degree in Data Science, Actuarial Science, Statistics, Mathematics, Computer Science, Business Analytics, or a related field (required).
• Master’s degree or postgraduate qualification in a relevant discipline is an added advantage.
• 5–8 years of progressive experience in data analytics, data engineering, or back-end software engineering.
• Experience in overseeing cross-functional teams.
• Proven track record of building and operationalising data systems and back-end platforms in enterprise or regulated environments.
• Demonstrated experience in delivering dashboards, automated pipelines, and predictive or scoring models in a commercial setting.
• Experience supporting analytics platforms, credit scoring, or decisioning systems within financial services is a strong advantage.
• Solid understanding of data governance, data warehousing, and regulatory compliance in the banking or financial sector.